PREDICTIVE MAINTENANCE MODELS FOR METAL-CUTTING MACHINE TOOLS

Abstract

Predictive maintenance (PdM) has gained significant attention in manufacturing industries as a proactive approach to minimizing machine tool failures, optimizing maintenance schedules, and reducing production downtime. Metal-cutting machine tools, which are integral to precision manufacturing, are susceptible to wear and mechanical degradation due to prolonged operation. Traditional maintenance strategies, such as reactive and preventive maintenance, are often inefficient in addressing unexpected breakdowns and unnecessary servicing. This study investigates the development of predictive maintenance models utilizing real-time sensor data, historical failure records, and machine learning algorithms to predict faults before they occur. Various predictive modeling techniques, including Random Forest, Support Vector Machines (SVM), and Neural Networks, are employed to analyze sensor signals such as vibration, acoustic emission, and thermal data. Additionally, this research integrates Industrial Internet of Things (IIoT) platforms for real-time monitoring and automated maintenance decision-making. The proposed predictive maintenance framework improves machine reliability, extends tool life, and enhances overall manufacturing efficiency by enabling data-driven fault detection and prevention strategies.